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Patterns of white matter degeneration in remotebrain areas from the basal ganglion lesion ofischemic stroke  patients  with motor impairmentXuejin Cao 

Southeast University https://orcid.org/0000-0002-3684-8092Zan Wang 

Southeast University, Zhongda hosiptialXiaohui Chen 

Southeast University Zhongda HospitalYanli Liu 

Southeast University Zhongda HospitalWei Wang 

Southeast University Zhongda HospitalIdriss Ali Abdoulaye 

Southeast UniversityXi Yang 

Southeast University Zhongda HospitalShiyao Zhang 

Southeast UniversityXiaoying Guo 

Southeast UniversityShanshan Wu 

Southeast UniversityYijing Guo  ( guoyijingseu@126.com )

Southeast University https://orcid.org/0000-0002-6638-8963

Research article

Keywords: Ischemic stroke, Diffusion tensor imaging, Structural network, Multivariate pattern analysis,Network-based statistic

DOI: https://doi.org/10.21203/rs.3.rs-36124/v2

License: This work is licensed under a Creative Commons Attribution 4.0 International License.  Read Full License

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AbstractBackground: Diffusion tensor imaging (DTI) studies have revealed distinct white matter characteristics ofthe brain following diseases. Beyond the lesion-symptom mapping, recent studies have demonstratedextensive structural and functional alterations of remote areas to local lesions caused by stroke in thebrain. Here, we further investigated the structural changes from a global level using DTI data throughmultivariate pattern analysis (MVPA) and network-based statistic (NBS).

Methods: Ten ischemic stroke patients with basal ganglia lesions and motor dysfunctions and elevendemographically matched adults as controls underwent brain Magnetic Resonance Imaging scans. DTIdata were processed to obtain fractional anisotropy (FA) maps and MVPA was used to explore brainregions that play an important role in classi�cation based on FA maps. The white matter (WM) structuralnetwork was constructed by the deterministic �ber tracking approach according to the AutomatedAnatomical Labeling (AAL) atlas. NBS was used to explore differences in structural networks betweengroups.

Results: MVPA applied to FA images correctly identi�ed stroke patients with a statistically signi�cantaccuracy of 100% (P≤0.001). Compared with the controls, the study patients showed FA reductions in theperilesional basal ganglia and brainstem, with a few showing reductions in bilateral frontal lobes. UsingNBS, we found a signi�cant decrease in FA-weighted WM subnetwork in stroke patients.

Conclusions: We identi�ed some patterns of WM degeneration affecting brain areas remote to theischemic lesion, revealing the abnormal organization of WM network in stroke patients, which may behelpful for the understanding of the neural mechanisms of stroke sequela. 

BackgroundFocal brain lesions could affect the overall performance of brain networks. In the past decades,numerous neuroimaging studies have investigated the structural and functional reorganizations afterstroke [1]. Diffusion tensor imaging (DTI) is commonly used to detect the structural integrity of the whitematter. Previous DTI researches in stroke patients calculated several diffusion tensor indicators (e.g.fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) ) in various regions along thecorticospinal tract (CST) of the lesioned hemisphere and contralateral hemisphere [2,3], or computed theCST integrity [4,5]. These imaging indicators were later correlated to function outcomes, furtherestablishing them as prediction markers. It has been discovered that certain correlations betweenchanges in speci�c �ber bundles and functional outcomes, such as aphasia [6], neglect [7], paralysis [8],etc. Besides the CST command of motor functions, the involvement of other �bers and brain areas inmovement control is not well understood. Therefore, neural degeneration in some regions might causespeci�c functional manifestations. We made a pattern recognition classi�cation of FA maps of the brainin an attempt to explore regions that are closely related to motor function.

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Multivariate pattern analysis (MVPA) is a method of imaging data analysis based on machine learningand pattern recognition. It involves the use of pattern classi�cation algorithms to extract spatial patternsfrom neuroimaging data for individual characteristics analysis [9]. MVPA has the advantage ofconsidering interregional correlations and searching for abnormalities throughout the entire brainbecause it adopts an unbiased and whole-brain method without the region of interest (ROI) beingarti�cially set [10]. This method has been widely used in psychiatry related studies to analyze mildchanges that can be observed in subclinical populations [11,12].

White matter structural connectivity can be modeled as a network. A network-based statistics (NBS)method is to control the family-wise error rate during mass univariate testing of every connection of thenetwork. NBS is also performed to investigate the interregional correlations of the brain on a global level[13,14]. We utilized these methods to explore subtle relevant changes and abnormalities of structuralnetworks of the brain for a further understanding of the intrinsic brain structural basis of ischemic strokepatients with residual motor dysfunctions.

MethodsEthics approval

The study was approved by the local Ethics Committee of the Southeast University a�liated ZhongdaHospital. All participants gave written informed consent to participate according to the Declaration ofHelsinki.

 

Participants  

Ten right-handed stroke patients (mean age 56.7±10.5 years) from the Southeast University a�liatedZhongda Hospital were recruited for this study from March 2019 to December 2019. The inclusion criteriafor patients were as follows:(1) age ≥20 and age ≤80 years (2) �rst-onset of ischemic stroke with theinvolvement of the basal ganglia; (3) pure motor de�cits; (4) stable conditions after treatment of acutestroke without recurrence. The exclusion factors: (1) a history of neurological or psychiatric disorders,prior or subsequent symptomatic stroke; (2) brain abnormalities unrelated to the infarct lesions; (3) MRIcontraindications. All of the affected extremities were evaluated for motor function. The motor outcomeof the affected limbs was evaluated by the Fugl-Meyer assessment (FMA), including the upper and lowerextremities [15]. Also, The Brunnstrom stage (BRS) was adopted. Recovery of the affected extremitieswas scored on a 6-point scale (1=severe; 6=normal) [16]. The clinical characteristics of the stroke patientsare summarized in Table 2. Eleven demographically matched healthy controls (mean age 61.5 ± 7.8years) were also recruited.

 

Image Acquisition

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Diffusion Tensor Images were acquired using a 3.0-Tesla Philips (Ingenia) Medical Systems equippedwith a Synergy-L Sensitivity Encoding (SENSE) head coil, using a single echo planar imaging (EPI)sequence, 33 diffusion-weighted images (b = 1000 s/mm2) and a reference T2-weighted image with nodiffusion weighting (b = 0 s/mm2) were obtained with the following acquisition parameters: voxel size=2×2×2 mm3, gap = 0 mm;echo time (TE) = 107 ms; repetition time (TR) = 5835 ms; �eld of view (FOV) =256 × 256 mm2 ; �ip angle (FA) = 90°; matrix = 128 × 128; slices = 75.

High-resolution T1-weighted axial images covering the whole brain were obtained by a 3D-magnetizationprepared rapid gradient-echo (MP-RAGE ) sequence: TR = 9.6 ms;TE = 3.7 ms; FA = 9°; matrix = 256 × 256;FOV = 256 × 256 mm2; voxel size =1×1×1 mm3; gap = 0 mm, number of slices = 140.  Additionally,sagittal �uid attenuated inversion recovery (FLAIR) images were obtained with the following parameters:TE = 110 ms, TR = 7000 ms, Inversion time (TI) = 2200 ms, FA= 90°, matrix size = 480 ×480, FOV = 250 ×250 mm2, slice thickness = 5 mm, slices= 20.  

 

Lesion mapping

Lesion-side normalization was performed to place lesions on the left side of the brain. For the patientswith lesions in the right hemisphere, the images were �ipped from the right to the left along themidsagittal line to simplify the comparison with the other patients with stroke. According to previousinvestigators, lesion masks of each patient were manually segmented on individual structural MRIimages (T1-weighted MP-RAGE and FLAIR images) using MRIcron software (http://www.mricro.com).After the spatial normalization of all individual lesion masks, a lesion overlap image for all patients wasconstructed (Fig. 1) [17,18,19].

 

DTI Data Pre-Processing and Network De�nition

DTI data analysis was performed by a pipeline toolbox for analyzing brain diffusion images (PANDA,http://www.nitrc.org/projects/panda) [20].The main procedure includes: (1) correcting for the headmotion and eddy current effects using the FMRIB’s Diffusion Toolbox (FDT); (2) calculating the diffusiontensor (DT) metric-----FA for each voxel using the DTIFIT tool; (3) normalizing: registrations of all theindividual FA images to the FMRIB58_FA template by calling the FNIRT tool [21]. The FA maps wereobtained and the FA values of bilateral CST were computed. Whole-brain tractography was performed foreach subject in the native diffusion space based on �ber assignment by continuous tracking (FACT)algorithm [22]. All voxels with fractional anisotropy (FA) ≥0.2 were used as seed points; the FA andcurvature thresholds of path tracing were set to 0.2 and 45°, respectively. 

  FA weighted networks were constructed from 116 nodes de�ned according to the AutomatedAnatomical Labeling (AAL) atlas [23]. The entire cerebral cortex was automatically partitioned into 116

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anatomical regions of interest (ROIs) (45 ROIs for each cerebral hemisphere, 26 ROIs for the cerebellum)using the AAL algorithm. The weight of the edges in the network was de�ned as the mean FA value of theconnected �bers between each pair nodes. In order to reduce false-positive connections, two nodes wereconsidered structurally connected only when at least 3 �bers were reconstructed between them [24].

 

Multivariate pattern analysis

Pattern classi�cation analysis could be used to examine the differences in the FA values between groups.A speci�c MVPA approach known as support vector machine (SVM) was implemented using PRoNTo(Pattern Recognition for Neuroimaging toolbox) software (http://www.mlnl.cs. ucl.ac.uk/pronto/) [25].Individual FA maps were treated as points located in a high dimensional space. A linear decisionboundary in this high dimensional space was de�ned by a hyperplane that separated the individual brainimages according to a class label (i.e., patients vs. controls)(Fig. 2A). A more detailed description of theSVM can be found in the previous reports [11]. The receiver operating characteristic curve (ROC),sensitivity and speci�city of the FA classi�cations and the weight of each brain region in theclassi�cation analysis were obtained. The brain regions with voxels that have values ≥30% of themaximum weight vector value of the discrimination map were taken as the key areas.

 

Statistical Analysis

The two-sample t tests were employed to test the group differences in demographic data and imagingmeasures. Testing differences in FA values of the CST between the affected and unaffected sides wereaccomplished with a paired t test. Correlations among the FA in key areas derived from MVPA and theCST were computed using Pearson’s correlation coe�cients. These statistical tests were performed usingthe Statistical Package for Social Science (SPSS) 22.0 software. The two-sample t test was adopted forthe inter-nodal connection comparisons followed by the NBS method using GRETNA (v2.0.0) to analyzethe FA networks between groups. Visualization of the results was performed by the BrainNet Viewer[26,27]. The comparisons of the two sets of FA values in the key areas derived from MVPA wereperformed in SPM12 with false discovery rate (FDR)-corrected (PFDR < 0.05, cluster size > 5) [28].

ResultsDemographic data and Patient characteristics

The demographic data of the patients and the controls are presented in Table 1. No signi�cantdifferences in age or gender were observed between the two groups. The mean interval from stroke onsetto DTI scans was 10.3 ± 9.0 weeks (Table 2). The lesions were mainly located in the basal ganglia region.

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For the 3 patients with lesions in the right hemisphere, the imaging data were laterally �ipped. Strokelesions for each patient were overlaid onto a T1 template in MNI standard space (Fig. 1).

The FA values of the ipsilesional CST in the patients were statistically signi�cant reduced than that of thecontralateral side (t=5.257, P=0.001). Compared with the controls, the FA values of the left (injured side)CST in the stroke group were signi�cantly decreased (t=6.511, P<0.001). And no signi�cant differences inthe FA values of the right CST (t=0.153, P=0.88) were observed between the two groups.

 

Overall Classi�er Performance

Figure 2 A shows the result of the MVPA classi�cation between the 10 stroke patients and the 11 controlsbased on FA values. The overall accuracy was 100% , and was signi�cant at P≤0.001 (P= 0.001). Boththe sensitivity and the speci�city were 100% (with ROC shown in Fig. 2B). This overall classi�cationaccuracy of the algorithm measures its ability to correctly sort the two groups.

 

Discrimination Map

In the whole-brain voxel weight maps, the weight vector value indicates the relative importance of thevoxel in the decision function ------ discrimination between patients and controls (Fig. 3). Note that allvoxels in the white matter mask contribute to the decision function since the analysis is multivariate. Thespatial distribution of the weight vector provided information about the contribution of different areas toclassi�cation.

The brain regions that contributed the most to the discrimination between stroke patients and controlswere identi�ed by setting the threshold to ≥30% of the maximum weight vector scores, consistent withprevious studies using MVPA for disease classi�cation [11,29]. The most identi�ed classi�ed features ofthe FA maps included the perilesional basal ganglia and brainstem, with a few in the bilateral frontallobes (Fig. 3 and 4 A), which were taken as the key areas.

By comparing the FA values of the two groups in the discrimination map by SPM12 (PFDR < 0.05, clustersize > 5), we found that the stroke group showed reduced FA in the key areas, shown in Figure 4A andreported in Table 3. According to the white matter atlas, the clusters were mainly located in the CSTpathway. Moreover, the FA values of these areas have a positive correlation with that of the ipsilesionalCST (r=0.888, p<0.001) (Fig. 4 B).

 

Decreased Connection of the Component Network in stroke patients

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FA weighted networks were constructed from the nodes (brain areas) de�ned according to the AAL atlas.The weight of the edges in the network was de�ned as the mean FA value of the connected �bersbetween each pair nodes. The NBS approach was used on the structural networks constructed bydeterministic tractography. We identi�ed several signi�cantly decreased connections of a componentnetwork (subnetwork) in stroke patients (P<0.01, P=0.008) including 26 regions and 32 connections(Table 4 and Fig. 5 A).

Figure 5 showed the subnetwork that reveals deterioration in the brain structure of the stroke patients.Table 3 reported the regions as nodes in the subnetwork. The backbone matrix of the group structuralmatrixes for each group of the subnetwork was calculated, including the 26 regions in Table 4 (Fig. 5 Band C). The subnetwork was composed of the cerebral cortical regions (the frontal lobe, the parietal lobeand the occipital lobe), the subcortical areas (the basal ganglion), and parts of the cerebellum. Comparedwith the results of MVPA, there were changes involving the frontal parietal lobe and the basal ganglia,and those in the brainstem region may overlap with some of the �bers connected to the cerebellum.

Discussion   Beyond the well-known concept of lesion-symptom mapping, some lesions in a single location in thebrain could disrupt brain functions routed to widespread neural networks [1,30]. Our analysis con�rmedthat local destruction of certain anatomic regions could affect remote areas in the brain.

 

White matter degeneration in the CST pathway of stroke patients

The degree of anisotropy depends on the level of organization, the integrity of the white matter tract, andthe degree of freedom of water diffusion movement caused by the oriented axonal membranes andmyelin sheaths [31]. Reduced anisotropy along the CST far from the original lesions has been interpretedas Wallerian degeneration (WD) [32]. DTI can quantify the FA values to evaluate the pathology change ofthe white matter, such as WD. Using the good classi�cation ability of MVPA, we reported that apart fromthe basal ganglia region which has direct infarcts, brain areas with signi�cantly decreased FA values werealso located in the brainstem of the lesioned hemisphere, and a few in the bilateral frontal lobes, whichmight be indicative of degenerative lesions caused by WD.  

The acute and chronic phases of stroke probably differ in the white matter changes since neural changescan contain anterograde and retrograde degeneration, or refactoring. However, no matter what kind ofalteration is present, it should generate speci�c structural changes and affect corresponding functions.Therefore we performed the pattern recognition classi�cation using the whole brain FA map and exploredthe  key brain regions important for distinguishing the stroke patients from the controls.

Our study veri�ed degenerative changes in the white matter of stroke patients. The infarcted lesions weremainly located in the basal ganglia region, but the FA reduction of some remote areas has reached the

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point where they can be differentiated from the controls. In conclusion, the damaged structual anatomyof the subcortical areas may induce the deterioration of the key white matter areas in the brain.

 

Decreased WM connections were widely distributed across brain regions of stroke patients  

Many patients were left with motor dysfunctions after the occurrence of cerebral infarction. Usually, it isrelated to the injury of the corticospinal tract. The CST originates from multiple motor andsomatosensory cortices including premotor cortex, supplementary motor cortex (SMA), primary motorcortex, as well as primary and secondary somatosensory cortices. The CST is crucial for proper executionof a volitional movement [33,34]. Apart from the CST and the motor areas of the cortex, the properexecution of movements involving balance and coordination also requires the extrapyramidal tract andother brain regions such as the nucleus in basal ganglia and the cerebellum [35]. The NBS analysisshowed that the structural subnetwork connection of the stroke group was weaker than that of the controlgroup, which indicate that the nerve �bers involved in the subnetwork were affected. When the integrityand order of the brain structure were destroyed, it can be re�ected in the white matter. Not only the brainregions directly related to the motor commands (e.g. precentral and SMA) but also other regions thatmight participate in the regulation of motor control were involved in the subnetwork.

In addition, since the MVPA analysis is multivariate, all voxels in the white matter mask contribute to thedecision function in the processing stage. We observed that the area where the left CST passes is reddish,and there is a light green distribution in the corpus callosum and some other areas (Fig. 3). Combinedwith the NBS results, the affected brain tissues were distributed across a wide range of brain regions (Fig.5). In short, the local abnormalities spread among the whole network as time elapses after the acutestage of the stroke onset. There is implicational information contained in the patterns of white matterdegeneration.

Next, we brie�y discuss the brain regions involved in the subnetwork (Table 4).

 

 The bilateral frontal lobe

Similar to previous studies results showing infarct-related focal thinning of the motor area in remotecortex via degeneration of inter-hemispheric connection �ber of the corpus callosum [36,37], we foundchanges in the connection between the frontal hemispheres, as well as reduced FA values in a small arealocated in the contralesional frontal cortex. It has been reported that secondary degeneration occurred inthe ipsilesional precentral gyrus after subcortical stroke involving the CST at the 6-month follow-up instroke patients by calculating the mean kurtosis (MK) value of manually drawing ROI from diffusionkurtosis imaging (DKI) imaging study [38]. We speculated that the degeneration in remote parts of theCST occurred in subacute phases of stroke.   

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In addition to motor-related brain areas, the affected brain areas in the frontal lobe may generate mild orlong-term cognitive changes in patients. Recently, a research reported that the Reading the Mind in theEyes Test (RMET) was associated with damages to the white-matter tracts connecting the frontal and thetemporo-parietal components of the RMET functional network [39]. Cognitive impairment still requiressensitive detection of subtle changes by complex experimental design or long-term observations [40].

 

The basal ganglia region

The patients were infarcted mainly in the basal ganglia region. The CST section passing through theposterior limb of the internal capsule may receive regulatory information from the nucleus of basalganglia or constitute loops within them. Due to the cortico-basal ganglia-thalamocortical ‘motor’ loop, anyimpact on the circuit constituent can lead to a shift in the balance between neural interactions in thedirect and indirect pathways and subsequently lead to variations in the brain functions [41,42]. In theearly stage of rehabilitation, stroke patients with hemiplegia often have synergistic movements, which are thought to be related to this loop.

 

The occipital lobe

In our study, changes in white matter connections involving the occipital (cuneus and precuneus) regionwere most likely related to visual effects. Voluntary actions modulate perception that follows theanatomical-functional bias of the motor system. Therefore stroke patients with dysfunction of normalvoluntary movements may develop corresponding abnormal sensory modulations that gradually affectbrain structure [43]. A study indicated a reduction in functional connectivity (FC) between the motor andexecutive control and the visuospatial networks in patients with motor de�cits vs. healthy controls [44].This demonstrated the existence of FC between the visual cortex, the ipsilateral/ contralateral motorcortex and the cerebellum. We conformed that this decline in FC is probably accompanied by a decreasein white matter structural connectivity.

 

The cerebellum

The cerebellum is often related to balance adjustment and patients with hemiplegia after stroke usuallyhave problems of stability and coordination. The connections between the cerebrum and the cerebellumpass through the cerebral peduncle in the brainstem. So we assume that the weakening of thoseconnections is partially consistent with the decrease in FA in the brainstem. Previous DTI studies alsofound  decreased FA in midbrain of the stroke patients by manually plotting ROI [38]. A recent studyindicated that the cerebellum plays a role on residual motor output by facilitating cortical excitability inchronic stroke [45]. Changes in cortico-cerebellar structural connectivity are probably caused by brain

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injuries related to the neural �bers connecting the cerebellum. The inability to perform normal movementsmight gradually lead to abnormal balance that is re�ected in the decrease of cortico-cerebellarconnectivity.

 

Limitations and Expectations

Despite our signi�cant �ndings, this research is not free of limitations. First, the sample size is small.Second, the interval of DTI acquirement from stroke onset ranging from 2-24 weeks. Recovery of FA inpenumbra regions occurs most rapidly during the �rst two weeks following stroke, with continued slowincreases in FA for many weeks thereafter [46-47]. The phases of stroke rehabilitation may in�uencewhite matter organization considering that we only discussed stroke patients with motor impairments ina cross-sectional insight. Future work can be further explored by expanding the sample size anddynamically observing imaging changes from the acute phase to the recovery phase. Additionally,emphasis should be placed on the classi�cation and re�nement of clinical behaviors of stroke patients,with speci�c brain feature changes that correspond to the functional outcome.

A study on 132 stroke patients using rest-fMRI has revealed that although structural damage from strokeis focal, remote dysfunction can occur in regions of the brain distant from the lesions. These results linkkey organizational features of the brain networks to behavior in stroke [48]. To date, there is no large-scale post-stroke structural network analysis similar in quantity and method to the above functionalanalysis. In this study, only stroke patients with motor dysfunction were recruited for analysis using DTIdata. By MVPA and NBS, we detected the reduced FA values and abnormal white matter connections inthe brain of those patients from a global level.

We need to further investigate the structure changes of the brain or the functional network in the naturalprocess of different types of brain injuries. Then the diagnosis and prognosis can be more accuratelyassessed paving the way for better treatment selection and research methods.

ConclusionsIn summary, we found that there are multiple WM structural abnormalities in the affected brain areas ofstroke patients with motor impairment. Our study may provide a basis for further exploration of the neuralmechanism involved in residual motor de�cits in stroke patients.

Looking forward to the future, we will further compare the neural changes of well-recovered patients toprovide a basis for the development of adaptive rehabilitation training strategies.

 

Abbreviations

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DTI: Diffusion tensor imaging; MVPA: multivariate pattern analysis; NBS: network-based statistic; AAL:Automated Anatomical Labeling; FA: fractional anisotropy; MRI: Magnetic Resonance Imaging; SVM:support vector machine; WM: white matter; MD: mean diffusivity; RD: radial diffusivity; CST: corticospinaltract; ROI: region of interest ; MNI: Montreal Neurological Institute; FMA: Fugl-Meyer assessment; BRS:Brunnstrom stage; WD: Wallerian degeneration; DKI: Diffusion kurtosis imaging; RMET: Reading the Mindin the Eyes Test; FC: functional connectivity.

DeclarationsAvailability of data and materials

The data in the current study are available on reasonable request to the corresponding author.

Competing interests

The authors declare that they have no competing interests

Funding

This work was funded by the National Natural Science Foundation of China (grant number:6590000127,81801680), Nanjing health science and technology development special fund project (grant number:YKK18218 ).

Acknowledgements 

We thank all the patients and volunteers for participating in this study.

Authors' contributions

XJ.C. and YJ.G. conceived and designed the research. XJ.C., Z.W, XH.C. and YL.L. performed theexperiments and analyzed the data. X.Y. and W.W. provided technical assistance. SY.Z., XY.G. and SS.W.provided experimental assistance. XJ.C., IA.A. and YJ.G. wrote the manuscript. 

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TablesTable 1. Demographic data of two groups.

  Control group Stroke group P value

Number 11 10  

Gender(male/female) 10/1 7/3 0.22

Age(mean ± SD ,years) 61.5 ± 7.8 56.7±10.5 0.24

range(years) 50-73 35-74  

 

Table 2.  Patient characteristics.

ID Age (years) Side Localization of infarct BRS FMA

 

Scan time

 (week)

1 49 R BG 3 3 5 58 22

2 67 R BG 2 1 4 37 2

3 57 L BG PV 2 1 4 35 4

4 51 L BG PV 3 2 4 41 10

5 60 R BG CR 4 4 5 86 20

6 57 L BG CR 2 1 5 38 3

7 74 L BG CR PV 5 4 5 82 14

8 60 L BG 2 1 3 19 2

9 35 L BG 2 1 3 25 3

10 57 L BG 5 5 5 89 24

 

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Side: the hemisphere of lesions on brain; IC = internal capsule; CR = corona radiate; BG = basal ganglia;PV= periventricular ; BRS (Brunnstrom stage) : separate functional evaluation of proximal and distalportions of the upper and entire lower extremities.  FMA: Fugl-Meyer assessment (full score=100). Scantime : intervel of DTI acquirement from stroke onset.

Table 3. Report of FA decreased brain areas in patients compared with controls.

ID Voxel

size

Peak MNIcoordinate(x,y,z)

Peakintensity

Brain regions White matterregions

(voxel size)

1 36 -4  -36  -46 0.028 Pons; Medulla.L CST.L 20

2 114 -10  -20  -22 0.032 Midbrain; Pons.L CP.L

CST.L

57

53

3 112 -22  -6  16 0.027 Extra-Nuclear;

LentiformNucleus.L

PLIC.L

SCR.L

SFOF.L

ALIC.L

43

30

15

12

4 9 -14  -20  60 0.019 MFG.L Not in theatlas

 

5 7 24  -18  66 0.023 MFG.R Not in theatlas

 

 

ID : the index of the cluster ; Voxel size: the quantity of voxels in the cluster; Peak MNI coordinate: indicatethe location of voxel with maximum weight vector scores (also peak intensity) in each cluster. ClusterLocater in PANDA software was used to locate the cluster image according to JHU ICBM-DTI-81 White-Matter Labels. White matter atlas (voxel size): the atlas regions this cluster involves and the quantity ofvoxels in this cluster overlapped with each atlas region. MFG: Medial Frontal Gyrus; CP:Cerebral peduncle; PLIC: Posterior limb of internal capsule; SCR: Superior corona radiata ; SFOF:Superior fronto-occipitalfasciculus (could be a part of anterior internal capsule) ; ALIC: Anterior limb of internal capsule;L:Left;R:Right.

Table 4. The 26 brain regions of the subnetwork nodes.

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ID Region Abbreviation Side MNI coordinate

x y z

1 Precentral PreCG L -39 -6 51

2 Dorsolateral Frontal Superior gyrus SFGdor L -18 35 42

3 Orbital Frontal Superior ORBsup L -17 47 -13

4 Frontal Middle gyrus MFG L -33 33 35

5 Orbital Frontal Middle ORBmid L -31 50 -10

6 Opercular Frontal Inferior IFGoperc L -48 13 19

7 Supplementary Motor Area SMA L -5 5 61

8 Supplementary Motor Area SMA R 9 0 62

9 Frontal Superior Medial SFGmed L -5 49 31

10 Cuneus CUN L -6 -80 27

11 Occipital Superior gyrus SOG L -17 -84 28

12 Occipital Middle gyrus MOG L -32 -81 16

13 Occipital Inferior gyrus IOG L -36 -78 -8

14 Postcentral PoCG L -42 -23 49

15 Parietal Superior gyrus SPG L -23 -60 59

16 Parietal Inferior gyrus IPL L -43 -46 47

17 Precuneus PCUN L -7 -56 48

18 Paracentral Lobule PCL L -8 -25 70

19 Caudate CAU R 15 12 9

20 Putamen PUT L -24 4 2

21 Pallidum PAL L -18 0 0

22 Thalamus THA L -11 -18 8

23 Cerebellum Crus2 CRBLCrus2 L -29 -73 -38

24 Cerebellum 3 CRBL3 L -9 -37 -19

25 Cerebellum 4_5 CRBL45 L -15 -43 -17

26 Cerebellum 9 CRBL9 L -11 -49 -46